Abstract

Sentiment classification involves quantifying the affective reaction of a human to a document, media item or an event. Although researchers have investigated several methods to reliably infer sentiment from lexical, speech and body language cues, training a model with a small set of labeled datasets is still a challenge. For instance, in expanding sentiment analysis to new languages and cultures, it may not always be possible to obtain comprehensive labeled datasets. In this paper, we investigate the application of semi- supervised and transfer learning methods to improve performances on low resource sentiment classification tasks. We experiment with extracting dense feature representations, pre-training and manifold regularization in enhancing the performance of sentiment classification systems. Our goal is a coherent implementation of these methods and we evaluate the gains achieved by these methods in …

Date

2018

Authors

Rahul Gupta, Saurabh Sahu, Carol Espy-Wilson, Shrikanth Narayanan

Conference

2018 IEEE international conference on acoustics, speech and signal processing (ICASSP)

Pages

5109-5113

Publisher

IEEE